• Corpus ID: 233004517

Defending Against Image Corruptions Through Adversarial Augmentations

@article{Calian2022DefendingAI,
  title={Defending Against Image Corruptions Through Adversarial Augmentations},
  author={Dan Andrei Calian and Florian Stimberg and Olivia Wiles and Sylvestre-Alvise Rebuffi and Andr'as Gyorgy and Timothy A. Mann and Sven Gowal},
  journal={ArXiv},
  year={2022},
  volume={abs/2104.01086}
}
Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog. Recent methods that focus on this problem, such as AugMix and DeepAugment, introduce defenses that operate in expectation over a distribution of image corruptions. In contrast, the literature on `p-norm bounded perturbations focuses on defenses against worst-case corruptions. In this work, we reconcile both approaches by proposing AdversarialAugment, a… 

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